Call +91 97505 95595 or email info@mcal.global. Visit us at 613, Vision Flora, Pimple Saudagar, Pune 411027.

GenAI for Data Engineering Course Pune — Build production RAG pipelines and LLMOps infrastructure | MCAL Global

Built for Experienced Data Engineers · 30 Hours · 10+ Projects

From Data Mover to
AI Systems Architect
— in 30 Hours

Q: What Python level is required?

Proficient — you should have built data pipelines in Python before. We do not teach Python fundamentals.

You already know how to build pipelines. This course teaches you how to build the infrastructure that powers enterprise AI — production RAG pipelines, self-healing ETL, multi-agent orchestration, and LLMOps. No beginner theory. Pure engineering.

30 Hours · 17 Modules · 5 Phases Classroom (Pune) / Live Online All API costs included

LangChain LlamaIndex Pinecone Weaviate Milvus Airflow dbt Kafka RAGAS FastAPI AWS Bedrock pgvector

30 hrs

Hands-On Labs
No Beginner Theory

Technical Modules
5 Structured Phases

10+

Deployed Projects
Portfolio-Ready

$147K

–$179K US Salary
AI-Skilled Data Engineers

The Problem

Your Pipeline Expertise
Is Not Enough Anymore

Structured pipelines → Context injection pipelines

Sending clean, chunked, embedded, metadata-tagged context to an LLM is not ETL — it requires completely different architectural decisions around chunking strategy, embedding selection, retrieval scoring, and hallucination mitigation.

Data warehouses → Vector stores

The vector database market grew from $2.46B in 2024 and is projected to reach $10.6B by 2032 at 27.5% CAGR. Engineers who cannot build and tune vector retrieval pipelines are already behind.

ETL engineers → LLM pipeline engineers

The RAG market alone was valued at $2.33B in 2025 and is projected to reach $81.51B by 2035 at 42.7% CAGR. Every enterprise building a RAG system needs engineers who can design it.

The talent gap is real

There are 2.9M expected data-related job vacancies globally (Experian). Demand for engineers who can bridge traditional data infrastructure with GenAI stacks is growing faster than supply.

Two paths — the engineer who adapts to GenAI and the one left behind

The engineers closing this gap now

are the ones setting the architectural standards for the next five years.

What Sets This Apart

This Course vs. Generic AI Courses

Every feature that matters to a working data engineer, compared directly.

GenAI for Data Engineering vs Generic AI Courses — Feature Comparison
	✓ This Course GenAI for Data Engineering	Generic Online AI Course
Target Audience	Experienced DEs, MLOps, Architects	Beginners with no prior context
Hands-On Lab Ratio	✓ 45% hands-on lab time	— 10–15%, mostly theory
RAG Pipeline Depth	Production: chunking, re-ranking, hybrid retrieval, PII masking	Intro to RAG with toy datasets
LLMOps & Monitoring	✓ RAGAS, LangSmith, hallucination detection	— Not covered
Data Governance for AI	✓ RBAC, PII masking, compliance pipelines	— Not covered
Real-World Capstone	✓ End-to-end GenAI platform, all components integrated	Module-by-module exercises only
Tech Stack	LangChain, LlamaIndex, Pinecone, Milvus, Weaviate, Airflow, dbt, Kafka	Generic Python notebooks
Output	✓ 10+ deployed projects + capstone + certificate	Certificate only

The Career Economics

Market Numbers Engineers Can’t Ignore

71% of organizations now report regular use of GenAI in at least one business function (McKinsey 2025–26). The engineers building that infrastructure are commanding top-of-market compensation.

Role	US Salary Range (2026)	Demand Signal
Senior Data Engineer (AI-skilled)	$147,000 – $179,000	~50% YoY demand increase
Generative AI Engineer	$113,939 – $158,492 avg base	One of the fastest-growing roles in tech
GenAI Engineer (90th percentile)	Up to $179,000+	Top-of-market, rapidly expanding
Mid-level Data Engineer	$119,000 – $170,000	Established, stable demand

Sources: Motion Recruitment 2026; Coursera GenAI Salary Report Mar 2025; ZipRecruiter Feb 2026; 365 Data Science 2025

RAG Market

$2.33B → $81.51B

42.7% CAGR through 2035

Vector Database Market

$2.46B → $10.6B

27.5% CAGR through 2032

Generative AI Market

$20.9B → $136.7B

36.7% CAGR through 2030

The Project Arc

10+ Deployed Projects. Zero Toy Demos.

Every module produces a deployed, working artifact. By the capstone, you will have built more production-adjacent AI infrastructure than most engineers encounter in two years on the job.

Project 02

Self-Healing ETL Pipeline

An Airflow-orchestrated ETL pipeline that detects schema drift and API failures — uses an LLM to diagnose root cause and trigger automated recovery. No more 2 AM alerts.

Apache Airflow LangChain OpenAI PostgreSQL

★ Flagship Project
Project 04

RAG-Powered Enterprise Data Assistant

Multi-stage RAG over internal enterprise documents. Semantic chunking, metadata-filtered vector retrieval, hybrid BM25 + dense retrieval, and re-ranking. Target: retrieval latency under 200ms with measurable hallucination reduction.

Pinecone / Weaviate LlamaIndex RAGAS FastAPI

Project 10

Multi-Agent Orchestration System

A LangChain-powered framework where specialized agents (data retrieval, SQL, summarization, code execution) collaborate to resolve complex analytical queries autonomously.

LangChain LlamaIndex Streamlit Docker

Complete Project Portfolio You Will Ship

Data Augmentation Application

FastAPI + Streamlit + LLM API — synthetic training data with schema validation and bias checking.

OpenAI GPT-4FastAPIStreamlit

Self-Healing ETL Pipeline

Airflow DAG with LLM-driven root-cause diagnosis and automated recovery. No more 2 AM alerts.

AirflowLangChainPostgreSQL

Text-to-SQL Query Interface

Natural language interface over a data warehouse with guardrail layer and multi-table join support.

LangChainpgvectorSQL

RAG-Powered Enterprise Data Assistant

Flagship: production multi-stage RAG with hybrid retrieval and RAGAS evaluation dashboard.

PineconeLlamaIndexRAGAS

Real-Time Data Enrichment Service

Kafka + Spark Streaming with LLM-driven entity extraction and sentiment classification at scale.

KafkaSparkOpenAI

PDF & Unstructured Document Extractor

Production ingestion pipeline for PDFs, scanned images, and heterogeneous formats.

LangChainUnstructuredFastAPI

Automated Pipeline Code Generator

LLM generates dbt models and Airflow DAGs from plain English, with static analysis validation.

LangChainGitHub Copilotdbt

AI-Powered Data Quality Monitor

LLM-assisted quality framework with natural language anomaly narratives and Streamlit dashboard.

LangSmithMLflowStreamlit

PII Masking & Governance Pipeline

Enterprise pre-processing layer with RBAC, audit logging, and PII redaction confidence scoring.

PresidioLangChainFastAPI

Multi-Agent Orchestration System

Agents (retrieval, SQL, summarization, code) collaborate autonomously with fallback handling.

LangChainLlamaIndexDocker

CAP

Capstone: End-to-End GenAI Data Platform

Full lifecycle: ingestion → transformation → vectorization → retrieval → generation → monitoring. Cloud-hosted, publicly accessible, RAGAS evaluated.

Full StackCloud DeployedRAGAS Evaluated

Job-Ready Outcomes

What You Will Be Able To Do

Production-deployable, architecture-level outcomes — not abstract learning objectives.

Design and deploy production RAG pipelines with advanced chunking, hybrid BM25 + dense retrieval, metadata filtering, and re-ranking

Architect vector database infrastructure on Pinecone, Milvus, Weaviate, and pgvector — right tool based on scale, latency, and cost

Build self-healing, AI-augmented ETL pipelines using Airflow and dbt with LLM-driven anomaly diagnosis and automated recovery

Implement enterprise-grade LLMOps including RAGAS hallucination scoring, embedding drift detection, and cost monitoring

Design and enforce data governance for AI with PII masking before LLM API calls, RBAC on retrieval, and compliance lineage tracking

Orchestrate multi-agent AI systems using LangChain and LlamaIndex with production-grade failure handling and memory management

Integrate LLM capabilities into real-time streams using Kafka + Spark Structured Streaming without sacrificing throughput or latency

Deploy GenAI data pipelines on cloud AWS Bedrock/SageMaker, Azure OpenAI Service, and GCP Vertex AI — with Terraform IaC

Complete Curriculum

17 Modules · 5 Phases · Concept → Lab → Build

Phase 1

Foundations for AI-Native Data Engineering

Modules 1–3 · Establish the architectural mental model, master prompt engineering for data tasks, and build your first AI-augmented data application.

AI/ML to GenAI paradigm shift from the data engineer's lens

Context windows, token costs, and their impact on pipeline architecture

GenAI tech stack: embedding models, vector indices, orchestration layers, LLMOps

Production GenAI data stack architecture and decision framework

Lab Deliverable: Architecture diagram of a production GenAI data stack mapped to your existing infrastructure, with annotated decision points

Structured output enforcement with JSON schema and Pydantic validation

Few-shot SQL generation and chain-of-thought for anomaly diagnosis

Token cost optimization and context window management strategies

System prompt design for multi-tenant data applications

Lab Deliverable: Reusable prompt template library covering 8 core data engineering tasks, with documented performance benchmarks across GPT-4, Claude, and Gemini

Synthetic tabular data, time-series, and document corpus generation

Bias detection and distributional accuracy validation

FastAPI backend + Streamlit frontend full-stack integration

Schema-validated output pipelines for downstream ML use

Lab Deliverable: Data augmentation FastAPI application with Streamlit frontend, validation schema layer, and bias-checking module — fully deployed

Phase 2

Building AI-Augmented Pipelines

Modules 4–7 · Redesign the ETL lifecycle for AI augmentation, automate code generation, parse unstructured documents, and build Text-to-SQL interfaces.

LLM-assisted schema mapping and transformation logic generation

Anomaly detection with LLM-generated narrative explanations

Automated retry with structured root-cause analysis

Airflow LLM operators as first-class task types

Lab Deliverable: Self-healing Airflow DAG detecting simulated failures, calling LLM for diagnosis, executing recovery — with logged audit trail

GitHub Copilot and LangChain code agent patterns for pipeline generation

dbt model generation from plain-English transformation specifications

Spark job and SQL migration script automation with validation

Static analysis and unit test generation for LLM-produced code

Lab Deliverable: Automated pipeline code generator: input a plain-English spec, output a validated dbt model with generated unit tests

Multimodal LLM API integration for table and form extraction

PDF, DOCX, email, and scanned document processing pipelines

Post-extraction validation schemas and confidence scoring

Human-review queue routing for low-confidence extractions

Lab Deliverable: Document ingestion pipeline processing 5 heterogeneous file types with structured field extraction, schema validation, and routing

Schema-aware context injection and dialect-specific SQL generation

Multi-table join handling and ambiguous query resolution

Guardrail layer preventing destructive query execution

Multi-turn conversation memory with confidence-threshold escalation

Lab Deliverable: Deployed Text-to-SQL interface: multi-table query support, query logging, confidence-threshold escalation to human reviewer

Phase 3

Production RAG Architectures

Modules 8–9 · Deep-dive into vector databases and build a full production RAG pipeline from ingestion to evaluation.

Pinecone (managed), Weaviate (GraphQL hybrid), Milvus (billion-scale), Qdrant (filtered), pgvector (PostgreSQL-native)

Embedding dimensionality trade-offs and HNSW vs. IVF index selection

Metadata filtering, quantization for cost reduction, multi-tenancy isolation

RBAC enforcement at the retrieval layer

Lab Deliverable: Benchmark report comparing Recall@10, MRR, and query latency across Pinecone, Weaviate, and pgvector on 500K documents — with documented decision framework

Chunking: fixed-size, recursive, semantic, sliding-window — empirical comparison on retrieval quality

Hybrid retrieval: dense vector + BM25 + Reciprocal Rank Fusion

Cross-encoder re-ranking, query expansion, and HyDE (Hypothetical Document Embeddings)

RAGAS evaluation: faithfulness, answer relevance, and context recall scoring

Lab Deliverable: RAG-powered knowledge assistant with RAGAS dashboard showing faithfulness and context recall — with documented optimization decisions

Phase 4

LLMOps, Real-Time AI, and Governance

Modules 10–13 · Implement enterprise AI governance, build observable LLM infrastructure, enrich real-time streams, and understand fine-tuning trade-offs.

PII identification: names, SSNs, account numbers, medical identifiers

RBAC enforcement at vector retrieval layers

Data lineage tracking for LLM inputs and outputs

Compliance-ready audit logging and sensitive data output filtering

Lab Deliverable: PII masking pipeline with confidence scores, LLM interaction logging, and role-based retrieval restrictions

Token consumption monitoring and cost alerting per query

Response latency P95/P99 tracking and SLA enforcement

Embedding drift detection and model version management

A/B testing frameworks for LLM providers with LangSmith tracing

Lab Deliverable: LLMOps monitoring dashboard: token cost per query, hallucination rate trend, retrieval latency distribution, and embedding drift

Kafka + Spark Structured Streaming with LLM enrichment stages

Real-time entity extraction and sentiment classification at scale

At-least-once delivery guarantees and backpressure management

Stateful aggregation in AI-enriched streams with schema enforcement

Lab Deliverable: Real-time enrichment service: Kafka events ingested, LLM-enriched with entity tags and sentiment scores, written to data lake with latency monitoring

Fine-tune vs. RAG vs. prompt engineering cost/accuracy/latency trade-offs

LoRA and QLoRA fine-tuning on domain-specific datasets

GraphRAG, agentic RAG, and CoRAG emerging architectures

Multimodal RAG pipeline design and implementation patterns

Lab Deliverable: Decision framework: given an enterprise AI use case, identify the correct approach with quantified trade-off analysis

Phase 5

Multi-Agent Systems and Capstone

Modules 14–17 · Build multi-agent systems, deploy on cloud, survey the 2026 landscape, and ship your end-to-end GenAI data platform capstone.

Specialized agent design: retrieval, SQL, summarization, code execution agents

Short-term and long-term vector memory management

Tool registration, failure handling, and orchestration routing logic

Agentic data pipeline pattern replacing scheduled batch jobs

Lab Deliverable: Multi-agent data analysis system: agents retrieve data, write SQL, summarize findings, generate reports — with fallback handling

AWS SageMaker, Bedrock, and OpenSearch deployment patterns

Azure OpenAI Service and AI Search (vector) integration

GCP Vertex AI RAG Engine and BigQuery integration

Terraform/CDK IaC for AI pipelines with auto-scaling and cost monitoring

Lab Deliverable: Cloud-deployed RAG pipeline with Terraform-provisioned infrastructure, auto-scaling configuration, and cost monitoring dashboard

Multimodal RAG: image + text retrieval pipelines

AI-native data lakehouse patterns (Databricks MosaicML, Snowflake Cortex)

Knowledge graph + RAG hybrid architectures

Data mesh for AI: organizational model and governance implications

Lab Deliverable: Technical architecture proposal for one emerging pattern — system design, tool selection rationale, and production readiness considerations

Full lifecycle GenAI platform design and implementation

RAGAS evaluation with baseline and optimized metrics

Architecture decision record documenting every major design choice

Live demo and 30-minute technical Q&A to instructors and peers

Lab Deliverable: Deployed cloud-hosted GenAI solution + RAGAS report + Architecture decision record + Live presentation

The Stack

Tools You Will Use in Production

Not toy notebooks. The actual tools enterprises run in 2026.

Large Language Models

OpenAI GPT-4o Anthropic Claude Google Gemini Llama 3 / Mistral GitHub Copilot

Orchestration Frameworks

LangChain LlamaIndex Haystack

Vector Databases

Pinecone Weaviate Milvus Qdrant pgvector Chroma

Data Orchestration

Apache Airflow dbt Apache Spark Apache Kafka

LLMOps & Monitoring

LangSmith MLflow RAGAS Weights & Biases

Cloud & Deployment

AWS Bedrock Azure OpenAI GCP Vertex AI FastAPI Docker Terraform

Who Should Enroll

Built for Professionals Who Already Build

Data Engineers Building AI-Augmented Pipelines

You know Spark, Airflow, and dbt. Your team is now being asked to build a RAG system or integrate LLM calls into Kafka streams — and you want to do it right.

Prerequisites: Proficiency in Python, SQL, Spark, and at least one cloud platform. Familiarity with Airflow or similar orchestration.

Analytics Engineers & Senior Data Analysts

You own dbt models and data transformation logic. You want the architectural depth to contribute meaningfully to GenAI projects — not just consume outputs from models others built.

Prerequisites: Strong SQL, dbt experience, Python familiarity. No ML background required.

MLOps Engineers Expanding into LLMOps

You manage ML infrastructure and can deploy a scikit-learn model. Now your org is running LLMs in production and nobody knows how to monitor prompt drift or evaluate RAG retrieval quality.

Prerequisites: ML pipeline experience, model serving familiarity, Python proficiency.

Data Architects & Technical Leaders

You design the data strategy. You need deep enough understanding of GenAI data architecture to make vendor decisions, evaluate RAG build-vs-buy trade-offs, and define AI governance policies.

Prerequisites: Data architecture experience, familiarity with cloud data platforms.

This course is not right for you if:

You are new to data engineering and have not yet built production ETL pipelines
You are looking for an introduction to Python, SQL, or cloud computing
You want a theoretical AI survey without hands-on implementation
You are not willing to commit 8–10 hours per week for 3–4 weeks

Assessment & Certification

Transparent Assessment.
70% Pass Threshold.

Continuous Assessment — 70% of Final Grade

Module Quizzes20%

Lab Exercises30%

Mid-Course RAG Project20%

Final Capstone — 30% of Final Grade

Cloud-hosted, publicly accessible deployed GenAI pipeline

RAGAS evaluation report with cost analysis and architecture decision record

30-minute live demo and technical Q&A

Delivery Format

Flexible Formats for Working Professionals

Part-Time (Working Professional Track)

3 sessions/week, evenings or weekends — 2.5–3 hrs per session. 8–10 hrs/week over 3–4 weeks. 80% of participants complete while working full-time.

Full-Time Intensive

5-day immersive bootcamp for maximum concentration, peer interaction, and accelerated completion.

Corporate / Team Training

Custom scheduling, private cohort delivery, and optional lab customization using your organization's actual data stack. Teams of 4+ welcome.

Lab Environment Included

All LLM API keys provided — zero personal API costs
Pre-authenticated cloud accounts & pre-configured vector databases
24/7 lab access with recorded sessions for catch-up

80%complete while working full-time

10+production projects shipped per graduate

30hrsof production-grade lab time

45%of program time is hands-on labs

“The RAG pipeline optimization techniques from Module 9 cut our production hallucination rate from 22% to under 6% — in two weeks.”

— Senior Data Engineer, Financial Services

Technical FAQ

Questions Engineers Actually Ask

You need to be comfortable writing and debugging Python scripts — proficient, not an expert. You should have built data pipelines in Python before. We do not teach Python fundamentals; we teach how to use Python to orchestrate LLMs, vector databases, and streaming pipelines.

No. You need data engineering experience, not ML experience. We teach you how to use LLMs as infrastructure components, not how to train them.

This course is built around the data engineer's workflow. Every lab assumes you already know how to build pipelines — and teaches you how to extend them with AI. We do not cover "what is machine learning." We teach you how to build RAG pipelines, govern LLM data flows, and monitor production AI systems.

Zero during the program. The lab environment includes pre-provisioned API access to all required LLM and vector database services. Optional personal experimentation beyond lab requirements may incur minor costs if you choose.

We cover a multi-layer approach: semantic chunking for higher retrieval precision, metadata-filtered retrieval to scope context, hybrid BM25 + dense retrieval for recall, cross-encoder re-ranking for precision, RAGAS faithfulness scoring for evaluation, and system prompt-level guardrails for response grounding. You measure the impact of each layer empirically.

Yes. Module 8 benchmarks pgvector directly against Pinecone and Weaviate on a 500K-document corpus — including when extending existing PostgreSQL infrastructure outweighs the performance advantages of a dedicated vector store.

The pipeline monitors Airflow run logs, detects failure signatures (schema changes, volume drops, API timeouts), calls an LLM with structured context about the failure, receives a structured diagnosis and recommended action, and executes it — logging every decision to an audit table. You build and test this against simulated failures.

Yes. The corporate training track accommodates customization of labs to use your organization's actual data infrastructure. Teams of 4+ can contact us for tailored delivery.

Employers care about what you can build and your ability to defend architectural decisions. You graduate with 10+ deployed artifacts, a live capstone, and RAGAS evaluation reports demonstrating measurable outcomes — materials that carry significantly more weight in technical hiring than a credential alone.

Sessions are recorded with 24/7 lab access. However, lab exercises are timed and the mid-course project has a fixed submission date. Extended absences can be accommodated by deferring to the next cohort at no penalty.

Full refund within 7 days of enrollment. 50% refund before Week 2. No refund after Week 2, as lab environments are fully provisioned and instruction is in progress.

Call +91 97505 95595 or email info@mcal.global. You can also visit us at 613, Vision Flora, Pimple Saudagar, Pune 411027.

Why MCAL Global